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Robyn L. Miller
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- affiliation: University of New Mexico, Albuquerque, USA
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2020 – today
- 2024
- [j20]David Sutherland Blair
, Robyn L. Miller, Vince D. Calhoun:
A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia. Entropy 26(7): 545 (2024) - [c51]Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun:
Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis. ISBI 2024: 1-5 - [c50]Sir-Lord Wiafe, Ashkan Faghiri, Zening Fu, Robyn L. Miller, Vince D. Calhoun:
Capturing Stretching and Shrinking of Inter-Network Temporal Coupling in FMRI Via WARP Elasticity. ISBI 2024: 1-4 - [c49]Robyn L. Miller, Victor M. Vergara, Vince D. Calhoun:
Markov Spatial Flows in Bold FMRI: A Novel Lens on the Bold Signal Applied To an Imaging Study of Schizophrenia. SSIAI 2024: 13-16 - [c48]Natalia Maksymchuk
Distribution of Connectivity Strengths Across Functional Regions has Higher Entropy in Schizophrenia Patients than in Controls. SSIAI 2024: 37-40 - [c47]Yutong Gao, Charles A. Ellis, Vince D. Calhoun, Robyn L. Miller:
Improving Age Prediction: Utilizing LSTM-Based Dynamic Forecasting For Data Augmentation in Multivariate Time Series Analysis. SSIAI 2024: 125-128 - [c46]Charles A. Ellis
Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures. xAI (4) 2024: 102-124 - [i7]Bradley T. Baker, Barak A. Pearlmutter, Robyn L. Miller, Vince D. Calhoun, Sergey M. Plis:
Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse. CoRR abs/2402.06751 (2024) - [i6]Bishal Thapaliya, Robyn L. Miller, Jiayu Chen, Yu-Ping Wang, Esra Akbas, Ram Sapkota, Bhaskar Ray, Pranav Suresh, Santosh Ghimire, Vince D. Calhoun, Jingyu Liu:
DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks. CoRR abs/2405.15805 (2024) - 2023
- [j19]Mohammad S. Eslampanah Sendi
Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease. Brain Connect. 13(6): 334-343 (2023) - [j18]Yutong Gao
Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment. Comput. Biol. Medicine 161: 107005 (2023) - [j17]Charles A. Ellis, Mohammad S. Eslampanah Sendi, Rongen Zhang, Darwin A. Carbajal, May D. Wang, Robyn L. Miller, Vince D. Calhoun
Novel methods for elucidating modality importance in multimodal electrophysiology classifiers. Frontiers Neuroinformatics 17 (2023) - [c45]Abhinav Sattiraju, Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun:
An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-Based Schizophrenia Diagnosis. BIBE 2023: 255-259 - [c44]Charles A. Ellis, Abhinav Sattiraju, Robyn L. Miller, Vince D. Calhoun:
Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data. BIBM 2023: 2466-2473 - [c43]Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun:
Improving Explainability for Single-Channel EEG Deep Learning Classifiers via Interpretable Filters and Activation Analysis. BIBM 2023: 2474-2481 - [c42]Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis. EMBC 2023: 1-4 - [c41]Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis. EMBC 2023: 1-4 - [c40]Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations. EMBC 2023: 1-4 - [c39]Haleh Falakshahi, Hooman Rokham, Robyn L. Miller, Jean Liu, Vince D. Calhoun
Network Differential in Gaussian Graphical Models from Multimodal Neuroimaging Data. EMBC 2023: 1-6 - [c38]Robyn L. Miller, Victor M. Vergara, Vince D. Calhoun
Hyperlocal Spatial Flows in BOLD fMRI Expose Novel Brain-Based Correlates of Schizophrenia. EMBC 2023: 1-4 - [c37]Charles A. Ellis, Abhinav Sattiraju, Robyn L. Miller, Vince D. Calhoun
Novel Approach Explains Spatio-Spectral Interactions In Raw Electroencephalogram Deep Learning Classifiers. ICASSP Workshops 2023: 1-5 - [c36]Robyn L. Miller, Victor M. Vergara, Helen Petropoulos, Vince D. Calhoun
Local Spatial Flow Strengths in Bold FMRI are Strongly Impacted by Schizophrenia. ICASSP Workshops 2023: 1-4 - [c35]Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
Identifying Neuropsychiatric Disorder Subtypes and Subtype-Dependent Variation in Diagnostic Deep Learning Classifier Performance. ISBI 2023: 1-4 - [c34]Noah Lewis, Armin Iraji, Robyn L. Miller, Vince D. Calhoun
Topological Correction of Subject-Level Intrinsic Connectivity Networks. ISBI 2023: 1-4 - [i5]Yutong Gao, Charles A. Ellis, Vince D. Calhoun
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis. CoRR abs/2312.08383 (2023) - 2022
- [j16]Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
A Systematic Approach for Explaining Time and Frequency Features Extracted by Convolutional Neural Networks From Raw Electroencephalography Data. Frontiers Neuroinformatics 16 (2022) - [c33]Charles A. Ellis, Martina Lapera Sancho, Mohammad S. Eslampanah Sendi, Robyn L. Miller, Vince D. Calhoun
Exploring Relationships between Functional Network Connectivity and Cognition with an Explainable Clustering Approach. BIBE 2022: 293-296 - [c32]Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
An Approach for Estimating Explanation Uncertainty in fMRI dFNC Classification. BIBE 2022: 297-300 - [c31]Charles A. Ellis, Abhinav Sattiraju, Robyn L. Miller, Vince D. Calhoun
Examining Effects of Schizophrenia on EEG with Explainable Deep Learning Models. BIBE 2022: 301-304 - [c30]Charles A. Ellis, Abhinav Sattiraju, Robyn L. Miller, Vince D. Calhoun
Examining Reproducibility of EEG Schizophrenia Biomarkers Across Explainable Machine Learning Models. BIBE 2022: 305-308 - [c29]Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun:
A Model Visualization-based Approach for Insight into Waveforms and Spectra Learned by CNNs. EMBC 2022: 1643-1646 - [c28]Mohammad S. Eslampanah Sendi, Robyn L. Miller, David H. Salat, Vince D. Calhoun:
A two-step clustering-based pipeline for big dynamic functional network connectivity data. EMBC 2022: 3741-3744 - [c27]Charles A. Ellis, Mohammad S. Eslampanah Sendi, Robyn L. Miller, Vince D. Calhoun:
An Unsupervised Feature Learning Approach for Elucidating Hidden Dynamics in rs-fMRI Functional Network Connectivity. EMBC 2022: 4449-4452 - [c26]Michael Weeks, Vince D. Calhoun, Robyn L. Miller:
Comparison of Energy Signals from the 4D DWT of Resting State FMRI Data Obtained from a Study on Schizophrenia. EMBC 2022: 4635-4640 - [c25]Yutong Gao, Vince D. Calhoun, Robyn L. Miller:
Transient Intervals of Significantly Different Whole Brain Connectivity Predict Recovery vs. Progression from Mild Cognitive Impairment: New Insights from Interpretable LSTM Classifiers. EMBC 2022: 4645-4648 - [i4]Eloy Geenjaar, Amrit Kashyap, Noah Lewis, Robyn L. Miller, Vince D. Calhoun:
Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data. CoRR abs/2205.13640 (2022) - [i3]Eloy Geenjaar, Noah Lewis, Amrit Kashyap, Robyn L. Miller, Vince D. Calhoun
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs. CoRR abs/2210.03667 (2022) - 2021
- [j15]Mohammad S. Eslampanah Sendi
Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder. Brain Connect. 11(10): 838-849 (2021) - [j14]Zening Fu
Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia. NeuroImage 224: 117385 (2021) - [c24]Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
A Novel Local Explainability Approach for Spectral Insight into Raw EEG-based Deep Learning Classifiers. BIBE 2021: 1-6 - [c23]Charles A. Ellis, Rongen Zhang
A Gradient-based Approach for Explaining Multimodal Deep Learning Classifiers. BIBE 2021: 1-6 - [c22]Charles A. Ellis, Rongen Zhang
A Novel Local Ablation Approach for Explaining Multimodal Classifiers. BIBE 2021: 1-6 - [c21]Charles A. Ellis, Mohammad S. Eslampanah Sendi, Robyn L. Miller, Vince D. Calhoun:
A Novel Activation Maximization-based Approach for Insight into Electrophysiology Classifiers. BIBM 2021: 3358-3365 - [c20]Charles A. Ellis, Rongen Zhang
Explainable Sleep Stage Classification with Multimodal Electrophysiology Time-series*. EMBC 2021: 2363-2366 - [c19]Robyn L. Miller, Victor M. Vergara, Vince D. Calhoun
Multiframe Evolving Dynamic Functional Network Connectivity Motifs (Evodfncs) from Continuity-Preserving Planar Embedding. EMBC 2021: 3066-3069 - [c18]Robyn L. Miller, Victor M. Vergara, Vince D. Calhoun
A Method for Integrative Analysis of Local and Global Brain Dynamics. EMBC 2021: 3189-3192 - [c17]Noah Lewis, Robyn L. Miller, Harshvardhan Gazula, Md Mahfuzur Rahman
Can recurrent models know more than we do? ICHI 2021: 243-247 - [c16]Shile Qi, Sergey M. Plis, Robyn L. Miller, Rogers F. Silva
3-way Parallel Fusion of Spatial (sMRI/dMRI) and Spatio-temporal (fMRI) Data with Application to Schizophrenia. ISBI 2021: 1577-1581 - [i2]Charles A. Ellis, Mohammad S. Eslampanah Sendi, Sergey M. Plis, Robyn L. Miller, Vince D. Calhoun
Algorithm-Agnostic Explainability for Unsupervised Clustering. CoRR abs/2105.08053 (2021) - 2020
- [c15]Elaheh Zendehrouh, Mohammad S. Eslampanah Sendi, Jing Sui
Aberrant Functional Network Connectivity Transition Probability in Major Depressive Disorder. EMBC 2020: 1493-1496 - [c14]Robyn L. Miller, Vince D. Calhoun
Hybrid dictionary learning-ICA approaches built on novel instantaneous dynamic connectivity metric provide new multiscale insights into dynamic brain connectivity. Medical Imaging: Image Processing 2020: 113131V - [c13]Robyn L. Miller, Vince D. Calhoun:
Transient Spectral Peak Analysis Reveals Distinct Temporal Activation Profiles for Different Functional Brain Networks. SSIAI 2020: 108-111 - [c12]Mohammad S. Eslampanah Sendi, Elaheh Zendehrouh, Zening Fu, Babak Mahmoudi, Robyn L. Miller, Vince D. Calhoun
A Machine Learning Model for Exploring Aberrant Functional Network Connectivity Transition in Schizophrenia. SSIAI 2020: 112-115
2010 – 2019
- 2019
- [j13]Barnaly Rashid, Jiayu Chen, Ishtiaque Rashid, Eswar Damaraju, Jingyu Liu
A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study. NeuroImage 184: 843-854 (2019) - 2018
- [j12]Flor A. Espinoza, Jessica A. Turner, Victor M. Vergara, Robyn L. Miller, Eva Mennigen
Whole-Brain Connectivity in a Large Study of Huntington's Disease Gene Mutation Carriers and Healthy Controls. Brain Connect. 8(3): 166-178 (2018) - [j11]Oktay Agcaoglu, Robyn L. Miller, Andrew R. Mayer, Kenneth Hugdahl
Corrigendum to "Lateralization of resting state networks and relationship to age and gender" [NeuroImage 104 (2015) 310-325]. NeuroImage 167: 504 (2018) - [j10]Catie Chang, Shella D. Keilholz, Robyn L. Miller, Mark W. Woolrich
Mapping and interpreting the dynamic connectivity of the brain. NeuroImage 180(Part): 335-336 (2018) - [j9]Hua Xie
Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study. NeuroImage 180(Part): 495-504 (2018) - [c11]Robyn L. Miller, Vince D. Calhoun
Dynamic Whole Brain Polarity Regimes Strongly Distinguish Controls from Schizophrenia Patients. PRNI 2018: 1-4 - [i1]A. Hunter, Bryan A. Moore, Maruti Kumar Mudunuru, Viet T. Chau, Robyn L. Miller, Roselyne B. Tchoua, C. Nyshadham, Satish Karra, Dan O'Malley, Esteban Rougier, Hari S. Viswanathan, Gowri Srinivasan:
Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications. CoRR abs/1806.01949 (2018) - 2017
- [j8]Anees Abrol
Replicability of time-varying connectivity patterns in large resting state fMRI samples. NeuroImage 163: 160-176 (2017) - [c10]Robyn L. Miller, Bryan A. Moore, Hari S. Viswanathan, Gowri Srinivasan:
Image Analysis Using Convolutional Neural Networks for Modeling 2D Fracture Propagation. ICDM Workshops 2017: 979-982 - 2016
- [j7]Barnaly Rashid
Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. NeuroImage 134: 645-657 (2016) - [j6]Robyn L. Miller
Cross-Frequency rs-fMRI Network Connectivity Patterns Manifest Differently for Schizophrenia Patients and Healthy Controls. IEEE Signal Process. Lett. 23(8): 1076-1080 (2016) - [j5]Robyn L. Miller, Victor M. Vergara, David B. Keator
A Method for Intertemporal Functional-Domain Connectivity Analysis: Application to Schizophrenia Reveals Distorted Directional Information Flow. IEEE Trans. Biomed. Eng. 63(12): 2525-2539 (2016) - [c9]Maziar Yaesoubi, Robyn L. Miller, Tülay Adali, Vince D. Calhoun:
Time-varying frequency modes of resting fMRI brain networks reveal significant gender differences. ICASSP 2016: 6310-6314 - 2015
- [j4]Oktay Agcaoglu, Robyn L. Miller, Andy R. Mayer, Kenneth Hugdahl
Lateralization of resting state networks and relationship to age and gender. NeuroImage 104: 310-325 (2015) - [j3]Maziar Yaesoubi, Robyn L. Miller, Vince D. Calhoun
Mutually temporally independent connectivity patterns: A new framework to study the dynamics of brain connectivity at rest with application to explain group difference based on gender. NeuroImage 107: 85-94 (2015) - [j2]Qingbao Yu
Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia. NeuroImage 107: 345-355 (2015) - [j1]Maziar Yaesoubi, Elena A. Allen, Robyn L. Miller, Vince D. Calhoun
Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information. NeuroImage 120: 133-142 (2015) - [c8]Mustafa S. Çetin, Jon M. Houck
Multimodal based classification of schizophrenia patients. EMBC 2015: 2629-2632 - [c7]Victor M. Vergara, Eswar Damaraju, Andrew B. Mayer, Robyn L. Miller, Mustafa S. Çetin, Vince D. Calhoun
The impact of data preprocessing in traumatic brain injury detection using functional magnetic resonance imaging. EMBC 2015: 5432-5435 - [c6]Robyn L. Miller, Victor M. Vergara, Vince D. Calhoun
Large scale fusion of brain imaging modalities and features using Markov-style dynamics in a feature meta-space. EMBC 2015: 7716-7719 - [c5]Barnaly Rashid, Mohammad R. Arbabshirani, Eswar Damaraju, Robyn L. Miller, Mustafa S. Çetin, Godfrey D. Pearlson, Vince D. Calhoun:
Classification of schizophrenia and bipolar patients using static and time-varying resting-state FMRI brain connectivity. ISBI 2015: 251-254 - 2014
- [c4]Robyn L. Miller, Maziar Yaesoubi, Vince D. Calhoun
Higher dimensional analysis shows reduced dynamism of time-varying network connectivity in schizophrenia patients. EMBC 2014: 3837-3840 - [c3]Shruti Gopal, Robyn L. Miller, Andrew Michael, Tülay Adali, Stefi A. Baum, Vince D. Calhoun:
A study of spatial variation in fMRI brain networks via independent vector analysis: Application to schizophrenia. PRNI 2014: 1-4 - [c2]Robyn L. Miller, Maziar Yaesoubi, Vince D. Calhoun, Shruti Gopal:
Higher dimensional fMRI connectivity dynamics show reduced dynamism in schizophrenia patients. PRNI 2014: 1-4 - 2013
- [c1]Vince D. Calhoun, Maziar Yaesoubi, Barnaly Rashid, Robyn L. Miller:
Characterization of connectivity dynamics in intrinsic brain networks. GlobalSIP 2013: 831-834
Coauthor Index
[j20] [c51] [c50] [c49] [c48] [c47] [c46] [i7] [i6] [j19] [j18] [j17] [c45] [c44] [c43] [c42] [c41] [c40] [c39] [c38] [c37] [c36] [c35] [c34] [i5] [j16] [c33] [c32] [c31] [c30] [c29] [c28] [c27] [c26] [c25] [i4] [i3] [j15] [j14] [c24] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [c16] [i2] [c15] [c14] [c13] [c12] [j13] [j12] [j11] [j9] [c11] [j8] [j7] [j6] [j5] [c9] [j4] [j3] [j2] [j1] [c8] [c7] [c6] [c5] [c4] [c3] [c2] [c1]
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last updated on 2024-10-23 20:36 CEST by the dblp team
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